Anvilogic vs. Splunk
Automate your SOC whether you keep Splunk or not
Splunk runs detection on the data you index into Splunk, so your license sets the ceiling on your coverage. Anvilogic automates work across data onboarding, search, detection, and investigation on every repository holding your security data, including Splunk, so you can keep it, move off it, or run both while you decide.
See how Anvilogic compares to Splunk on the data you already have →
Go beyond detection and investigation
In a Splunk-only SOC, the license decides which sources get indexed. Everything else gets filtered, sampled, or dropped. Anvilogic runs security operations above the storage layer, so Splunk keeps the data where it earns its keep, high-volume telemetry moves to a data lake or object storage, and the work covers both.
Anvilogic connects that work across four core areas of security operations:
Make new security data usable faster.
Profile and map new feeds, understand schemas, and identify coverage opportunities without centralizing the data first.
Learn more →Search across the data you already have.
Query across SIEMs, data lakes, cloud storage, and security platforms without copying everything into one index.
Learn more →Turn gaps into coverage.
Identify gaps, build and tune detections, and deploy them into the systems where your data already lives, in each platform's native syntax.
Learn more →Move from alert to decided case.
Move from alert to decided case with contextual triage and investigation.
Learn more →At a glance
| Splunk | Anvilogic | |
|---|---|---|
| Detection execution | Runs in Splunk, on data indexed into Splunk | Runs natively in Splunk, Sentinel, Snowflake, Databricks, and more |
| Query language | SPL | SPL, KQL, SQL, and native platform syntax |
| Search | The Splunk index, plus federated access to S3 and other Splunk deployments | Federated across Splunk, Sentinel, Elastic, LogScale, Snowflake, Databricks, Azure, and S3 |
| What you stand up first | Ingest and indexing into Splunk before you can detect on a source | Access to the repositories you already run |
| Cost model | Ingest volume or workload capacity, both scaling with what you centralize | Flat platform fee plus AI credits, with compute charged on the data each query and detection actually touches |
| Detection content | ESCU content maintained for the Splunk platform | Thousands of MITRE-mapped detections maintained by a dedicated threat research team |
| Detection authoring | SPL correlation searches, rebuilt if a source moves | Low code builder, detection-as-code with version control, and MCP |
| Automation model | Alerts route to SOAR or ticketing | Blueprints orchestrate workflows across the four core areas with human approval gates |
Response handoff | Splunk SOAR | Tines, Torq, ServiceNow, Jira, and more |
Why the difference matters
Stop letting the ingest bill set your detection strategy
When coverage depends on what is indexed, controlling cost means filtering, sampling, and dropping sources. Route high-volume telemetry to a data lake or object storage instead and detect on it where it lands. SAP did this and saved more than $1M annually without giving up a detection.
Keep Splunk or leave it. Coverage travels either way.
Detections written in SPL live and die with Splunk. Anvilogic holds detection above the storage layer and deploys the same logic into each platform's native language, so a migration does not stall behind a rule-by-rule rewrite and a data lake does not restart your coverage from zero. A global telecom operator is migrating off Splunk with Anvilogic on a five-week timeline while its SOC keeps running.
Give AI the context of your SOC
Anvilogic’s Enterprise Security Graph connects feeds, schemas, detections, alerts, tuning decisions, and investigation outcomes across every connected platform, so agents reason with the context of the whole environment, not the contents of one index.
Proven across complex enterprise environments
SAP kept Splunk, moved telemetry to Databricks, and ran detection across both. 35% more MITRE coverage in one week, 60 to 80% less detection engineering effort, and more than $1M saved annually.
"We were early adopters of the unified workflow Anvilogic and Databricks provide and have been able to bring detection engineering outcomes to business enablers recognized at the board level."
"Anvilogic is the perfect solution because it doesn't depend on any specific underlying data lake or SIEM. It isolates and abstracts the layer of data storage down to the schema."
Bring us a real test
Demonstrate onboarding, detection deployment, and case resolution on your existing platforms, Splunk included.
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